Safety Cages for Reliable Machine Learning

Abstract

Ensuring the reliability of machine learning models in safety-critical space missions remains a significant challenge, especially when ground-truth data is unavailable for real-time validation. While machine learning can augment pipelines by extracting transmission spectra from complex light curves, vulnerabilities to instrument anomalies and domain shifts introduce risks. This study evaluates a modular safety cage architecture operating as a parallel monitoring layer to assess prediction validity without modifying the underlying estimator. By monitoring runtime indicators, including uncertainty quantification, out-of-domain detection, and influence functions, the framework constrains the operational domain to a verified region. The results demonstrate that model failure is multifaceted, requiring indicator fusion strategies, and that applying safety-driven rejection allows for a small reduction in data coverage leading to a significant reduction in prediction error. In addition, the framework is extended with an error-aware model specialisation pipeline that partitions the parameter space to deploy specialised local experts.